mca 或各种 ca（多变量分析）
我将对我公司的一些信息进行分析。
我想到制作一个 ca 来表示两个变量之间的关联。我有 3 个变量：类别、标签、定价。我的想法是进行两项分析，一项是查看类别 - Valorarion 之间的关联，第二项分析是标签 - Valoration 之间的关联。
但我认为这种表示方式可以通过 mca 实现。
你向我推荐什么？
谢谢
I will make an analysis about some information about my company.
I thought of making a ca to represent the association between two variables. I have 3 variables: Category, Tag, Valoration. My idea is to make 2 analyses, one to view the association between Category - Valorarion and a second analysis between Tag - Valoration.
But I think that this representation is possible with a mca.
What do you recommend to me?
Thank You
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（3）
各种分类或关联规则挖掘算法也可能有很大帮助。您可以查看用于机器学习和数据挖掘的 Weka 工具台。
Various classification or association rule mining algorithms could be of much help too. You could check the Weka toolbench for machine learning and data mining.
假设所有变量都是分类的，您可以使用多重分类分析来了解变量之间的关联。早在 2k7 时，欧洲政治联盟就有一篇关于这个主题的好文章，但我在我的硬盘上找不到它，我确信谷歌会在某个地方找到它。我无法“看到”你的数据，所以我不能肯定地说 MCA 会比回归或 GLM 更好，但我引用的文章专门讨论了 MCA 与 GLM 与 MCA 与 GLM 的关系回归。
或者，您可以使用皮尔逊积矩相关性来确定系数。接近1=正线性关系，接近-1=负线性关系，接近0=无线性关系。
Assuming that all variables are categorical, you can use multiple classification analysis to gain an understanding of the associations between the variables. There was a good article on the topic from the European Consortium for Politics back in 2k7 but I can't find it on my drive, I'm sure google will have it somewhere. I can't "see" your data so I can't say with any certainty that MCA will be better than regression or GLM but the article I'm referring to has a discussion on this topic specifically to do with MCA vs. GLM vs. Regression.
Alternatively, you could use pearson product-moment correlations to identify the coefficients. Close to 1 = positive linear relationship, close to -1 = negative linear relationship, close to 0 = no linear relationship.
我遇到了用于分类数据分析的 VGAM 包。你也可以检查一下
I came across VGAM package for categorical data analysis. You could check this too